Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations10324
Missing cells0
Missing cells (%)0.0%
Duplicate rows21
Duplicate rows (%)0.2%
Total size in memory1.5 MiB
Average record size in memory153.0 B

Variable types

Text4
Categorical9
Numeric6
Boolean1

Alerts

Dataset has 21 (0.2%) duplicate rowsDuplicates
brand is highly overall correlated with dosage and 4 other fieldsHigh correlation
country is highly overall correlated with fulfill via and 2 other fieldsHigh correlation
dosage is highly overall correlated with brandHigh correlation
dosage form is highly overall correlated with brand and 2 other fieldsHigh correlation
freight cost (usd) is highly overall correlated with weightHigh correlation
fulfill via is highly overall correlated with brand and 2 other fieldsHigh correlation
product group is highly overall correlated with brand and 2 other fieldsHigh correlation
reliability is highly overall correlated with country and 1 other fieldsHigh correlation
shipment mode is highly overall correlated with country and 1 other fieldsHigh correlation
sub classification is highly overall correlated with brand and 2 other fieldsHigh correlation
vendor inco term is highly overall correlated with fulfill viaHigh correlation
weight is highly overall correlated with freight cost (usd)High correlation
managed by is highly imbalanced (97.4%) Imbalance
product group is highly imbalanced (69.9%) Imbalance
brand is highly imbalanced (62.3%) Imbalance
weight is highly skewed (γ1 = 44.78575407) Skewed
dosage has 1736 (16.8%) zeros Zeros
Delivery Delay Risk has 6324 (61.3%) zeros Zeros

Reproduction

Analysis started2025-01-14 18:00:28.031583
Analysis finished2025-01-14 18:00:31.173498
Duration3.14 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct142
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2025-01-14T21:00:31.276171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters103240
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.1%

Sample

1st row100-CI-T01
2nd row108-VN-T01
3rd row100-CI-T01
4th row108-VN-T01
5th row108-VN-T01
ValueCountFrequency (%)
116-za-t30 768
 
7.4%
104-ci-t30 729
 
7.1%
151-ng-t30 628
 
6.1%
114-ug-t30 596
 
5.8%
108-vn-t30 522
 
5.1%
106-ht-t30 450
 
4.4%
111-mz-t30 431
 
4.2%
110-zm-t30 406
 
3.9%
109-tz-t30 369
 
3.6%
107-rw-t30 340
 
3.3%
Other values (132) 5085
49.3%
2025-01-14T21:00:31.471403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 20648
20.0%
1 18694
18.1%
0 16409
15.9%
T 11732
11.4%
3 8596
8.3%
Z 3815
 
3.7%
G 2289
 
2.2%
6 2032
 
2.0%
N 1981
 
1.9%
4 1852
 
1.8%
Other values (25) 15192
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 20648
20.0%
1 18694
18.1%
0 16409
15.9%
T 11732
11.4%
3 8596
8.3%
Z 3815
 
3.7%
G 2289
 
2.2%
6 2032
 
2.0%
N 1981
 
1.9%
4 1852
 
1.8%
Other values (25) 15192
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 20648
20.0%
1 18694
18.1%
0 16409
15.9%
T 11732
11.4%
3 8596
8.3%
Z 3815
 
3.7%
G 2289
 
2.2%
6 2032
 
2.0%
N 1981
 
1.9%
4 1852
 
1.8%
Other values (25) 15192
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 20648
20.0%
1 18694
18.1%
0 16409
15.9%
T 11732
11.4%
3 8596
8.3%
Z 3815
 
3.7%
G 2289
 
2.2%
6 2032
 
2.0%
N 1981
 
1.9%
4 1852
 
1.8%
Other values (25) 15192
14.7%

country
Categorical

High correlation 

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
South Africa
1406 
Nigeria
1194 
Côte d'Ivoire
1083 
Uganda
779 
Vietnam
688 
Other values (38)
5174 

Length

Max length18
Median length12
Mean length8.4762689
Min length4

Characters and Unicode

Total characters87509
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowCôte d'Ivoire
2nd rowVietnam
3rd rowCôte d'Ivoire
4th rowVietnam
5th rowVietnam

Common Values

ValueCountFrequency (%)
South Africa 1406
13.6%
Nigeria 1194
11.6%
Côte d'Ivoire 1083
10.5%
Uganda 779
 
7.5%
Vietnam 688
 
6.7%
Zambia 683
 
6.6%
Haiti 655
 
6.3%
Mozambique 631
 
6.1%
Zimbabwe 538
 
5.2%
Tanzania 519
 
5.0%
Other values (33) 2148
20.8%

Length

2025-01-14T21:00:31.545728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
south 1570
11.7%
africa 1406
 
10.5%
nigeria 1194
 
8.9%
côte 1083
 
8.1%
d'ivoire 1083
 
8.1%
uganda 779
 
5.8%
vietnam 688
 
5.1%
zambia 683
 
5.1%
haiti 655
 
4.9%
mozambique 631
 
4.7%
Other values (38) 3596
26.9%

Most occurring characters

ValueCountFrequency (%)
a 12282
 
14.0%
i 10247
 
11.7%
e 5522
 
6.3%
o 4473
 
5.1%
n 4350
 
5.0%
t 4323
 
4.9%
r 3874
 
4.4%
3044
 
3.5%
u 2914
 
3.3%
m 2777
 
3.2%
Other values (38) 33703
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87509
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 12282
 
14.0%
i 10247
 
11.7%
e 5522
 
6.3%
o 4473
 
5.1%
n 4350
 
5.0%
t 4323
 
4.9%
r 3874
 
4.4%
3044
 
3.5%
u 2914
 
3.3%
m 2777
 
3.2%
Other values (38) 33703
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87509
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 12282
 
14.0%
i 10247
 
11.7%
e 5522
 
6.3%
o 4473
 
5.1%
n 4350
 
5.0%
t 4323
 
4.9%
r 3874
 
4.4%
3044
 
3.5%
u 2914
 
3.3%
m 2777
 
3.2%
Other values (38) 33703
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87509
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 12282
 
14.0%
i 10247
 
11.7%
e 5522
 
6.3%
o 4473
 
5.1%
n 4350
 
5.0%
t 4323
 
4.9%
r 3874
 
4.4%
3044
 
3.5%
u 2914
 
3.3%
m 2777
 
3.2%
Other values (38) 33703
38.5%

managed by
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
PMO - US
10265 
South Africa Field Office
 
57
Haiti Field Office
 
1
Ethiopia Field Office
 
1

Length

Max length25
Median length8
Mean length8.0960868
Min length8

Characters and Unicode

Total characters83584
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPMO - US
2nd rowPMO - US
3rd rowPMO - US
4th rowPMO - US
5th rowPMO - US

Common Values

ValueCountFrequency (%)
PMO - US 10265
99.4%
South Africa Field Office 57
 
0.6%
Haiti Field Office 1
 
< 0.1%
Ethiopia Field Office 1
 
< 0.1%

Length

2025-01-14T21:00:31.607996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T21:00:31.662775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
pmo 10265
33.1%
10265
33.1%
us 10265
33.1%
office 59
 
0.2%
field 59
 
0.2%
africa 57
 
0.2%
south 57
 
0.2%
haiti 1
 
< 0.1%
ethiopia 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
20705
24.8%
O 10324
12.4%
S 10322
12.3%
M 10265
12.3%
P 10265
12.3%
- 10265
12.3%
U 10265
12.3%
i 179
 
0.2%
f 175
 
0.2%
e 118
 
0.1%
Other values (14) 701
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 83584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
20705
24.8%
O 10324
12.4%
S 10322
12.3%
M 10265
12.3%
P 10265
12.3%
- 10265
12.3%
U 10265
12.3%
i 179
 
0.2%
f 175
 
0.2%
e 118
 
0.1%
Other values (14) 701
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 83584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
20705
24.8%
O 10324
12.4%
S 10322
12.3%
M 10265
12.3%
P 10265
12.3%
- 10265
12.3%
U 10265
12.3%
i 179
 
0.2%
f 175
 
0.2%
e 118
 
0.1%
Other values (14) 701
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 83584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
20705
24.8%
O 10324
12.4%
S 10322
12.3%
M 10265
12.3%
P 10265
12.3%
- 10265
12.3%
U 10265
12.3%
i 179
 
0.2%
f 175
 
0.2%
e 118
 
0.1%
Other values (14) 701
 
0.8%

fulfill via
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
From RDC
5404 
Direct Drop
4920 

Length

Max length11
Median length8
Mean length9.4296784
Min length8

Characters and Unicode

Total characters97352
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect Drop
2nd rowDirect Drop
3rd rowDirect Drop
4th rowDirect Drop
5th rowDirect Drop

Common Values

ValueCountFrequency (%)
From RDC 5404
52.3%
Direct Drop 4920
47.7%

Length

2025-01-14T21:00:31.721718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T21:00:31.769997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
from 5404
26.2%
rdc 5404
26.2%
direct 4920
23.8%
drop 4920
23.8%

Most occurring characters

ValueCountFrequency (%)
r 15244
15.7%
D 15244
15.7%
10324
10.6%
o 10324
10.6%
F 5404
 
5.6%
m 5404
 
5.6%
R 5404
 
5.6%
C 5404
 
5.6%
i 4920
 
5.1%
e 4920
 
5.1%
Other values (3) 14760
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 15244
15.7%
D 15244
15.7%
10324
10.6%
o 10324
10.6%
F 5404
 
5.6%
m 5404
 
5.6%
R 5404
 
5.6%
C 5404
 
5.6%
i 4920
 
5.1%
e 4920
 
5.1%
Other values (3) 14760
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 15244
15.7%
D 15244
15.7%
10324
10.6%
o 10324
10.6%
F 5404
 
5.6%
m 5404
 
5.6%
R 5404
 
5.6%
C 5404
 
5.6%
i 4920
 
5.1%
e 4920
 
5.1%
Other values (3) 14760
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 15244
15.7%
D 15244
15.7%
10324
10.6%
o 10324
10.6%
F 5404
 
5.6%
m 5404
 
5.6%
R 5404
 
5.6%
C 5404
 
5.6%
i 4920
 
5.1%
e 4920
 
5.1%
Other values (3) 14760
15.2%

vendor inco term
Categorical

High correlation 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
N/A - From RDC
5404 
EXW
2778 
DDP
1443 
FCA
 
397
CIP
 
275
Other values (3)
 
27

Length

Max length14
Median length14
Mean length8.7578458
Min length3

Characters and Unicode

Total characters90416
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEXW
2nd rowEXW
3rd rowFCA
4th rowEXW
5th rowEXW

Common Values

ValueCountFrequency (%)
N/A - From RDC 5404
52.3%
EXW 2778
26.9%
DDP 1443
 
14.0%
FCA 397
 
3.8%
CIP 275
 
2.7%
DDU 15
 
0.1%
DAP 9
 
0.1%
CIF 3
 
< 0.1%

Length

2025-01-14T21:00:32.004984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T21:00:32.058759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
n/a 5404
20.4%
5404
20.4%
from 5404
20.4%
rdc 5404
20.4%
exw 2778
10.5%
ddp 1443
 
5.4%
fca 397
 
1.5%
cip 275
 
1.0%
ddu 15
 
0.1%
dap 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
16212
17.9%
D 8329
 
9.2%
C 6079
 
6.7%
A 5810
 
6.4%
F 5804
 
6.4%
N 5404
 
6.0%
/ 5404
 
6.0%
o 5404
 
6.0%
r 5404
 
6.0%
- 5404
 
6.0%
Other values (8) 21162
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16212
17.9%
D 8329
 
9.2%
C 6079
 
6.7%
A 5810
 
6.4%
F 5804
 
6.4%
N 5404
 
6.0%
/ 5404
 
6.0%
o 5404
 
6.0%
r 5404
 
6.0%
- 5404
 
6.0%
Other values (8) 21162
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16212
17.9%
D 8329
 
9.2%
C 6079
 
6.7%
A 5810
 
6.4%
F 5804
 
6.4%
N 5404
 
6.0%
/ 5404
 
6.0%
o 5404
 
6.0%
r 5404
 
6.0%
- 5404
 
6.0%
Other values (8) 21162
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16212
17.9%
D 8329
 
9.2%
C 6079
 
6.7%
A 5810
 
6.4%
F 5804
 
6.4%
N 5404
 
6.0%
/ 5404
 
6.0%
o 5404
 
6.0%
r 5404
 
6.0%
- 5404
 
6.0%
Other values (8) 21162
23.4%

shipment mode
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
Air
6473 
Truck
2830 
Air Charter
650 
Ocean
 
371

Length

Max length11
Median length3
Mean length4.1237892
Min length3

Characters and Unicode

Total characters42574
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAir
2nd rowAir
3rd rowAir
4th rowAir
5th rowAir

Common Values

ValueCountFrequency (%)
Air 6473
62.7%
Truck 2830
27.4%
Air Charter 650
 
6.3%
Ocean 371
 
3.6%

Length

2025-01-14T21:00:32.126350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T21:00:32.179014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
air 7123
64.9%
truck 2830
 
25.8%
charter 650
 
5.9%
ocean 371
 
3.4%

Most occurring characters

ValueCountFrequency (%)
r 11253
26.4%
A 7123
16.7%
i 7123
16.7%
c 3201
 
7.5%
T 2830
 
6.6%
u 2830
 
6.6%
k 2830
 
6.6%
a 1021
 
2.4%
e 1021
 
2.4%
C 650
 
1.5%
Other values (5) 2692
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42574
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 11253
26.4%
A 7123
16.7%
i 7123
16.7%
c 3201
 
7.5%
T 2830
 
6.6%
u 2830
 
6.6%
k 2830
 
6.6%
a 1021
 
2.4%
e 1021
 
2.4%
C 650
 
1.5%
Other values (5) 2692
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42574
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 11253
26.4%
A 7123
16.7%
i 7123
16.7%
c 3201
 
7.5%
T 2830
 
6.6%
u 2830
 
6.6%
k 2830
 
6.6%
a 1021
 
2.4%
e 1021
 
2.4%
C 650
 
1.5%
Other values (5) 2692
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42574
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 11253
26.4%
A 7123
16.7%
i 7123
16.7%
c 3201
 
7.5%
T 2830
 
6.6%
u 2830
 
6.6%
k 2830
 
6.6%
a 1021
 
2.4%
e 1021
 
2.4%
C 650
 
1.5%
Other values (5) 2692
 
6.3%

product group
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
ARV
8550 
HRDT
1728 
ANTM
 
22
ACT
 
16
MRDT
 
8

Length

Max length4
Median length3
Mean length3.1702828
Min length3

Characters and Unicode

Total characters32730
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHRDT
2nd rowARV
3rd rowHRDT
4th rowARV
5th rowARV

Common Values

ValueCountFrequency (%)
ARV 8550
82.8%
HRDT 1728
 
16.7%
ANTM 22
 
0.2%
ACT 16
 
0.2%
MRDT 8
 
0.1%

Length

2025-01-14T21:00:32.236602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T21:00:32.290240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
arv 8550
82.8%
hrdt 1728
 
16.7%
antm 22
 
0.2%
act 16
 
0.2%
mrdt 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
R 10286
31.4%
A 8588
26.2%
V 8550
26.1%
T 1774
 
5.4%
D 1736
 
5.3%
H 1728
 
5.3%
M 30
 
0.1%
N 22
 
0.1%
C 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 10286
31.4%
A 8588
26.2%
V 8550
26.1%
T 1774
 
5.4%
D 1736
 
5.3%
H 1728
 
5.3%
M 30
 
0.1%
N 22
 
0.1%
C 16
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 10286
31.4%
A 8588
26.2%
V 8550
26.1%
T 1774
 
5.4%
D 1736
 
5.3%
H 1728
 
5.3%
M 30
 
0.1%
N 22
 
0.1%
C 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 10286
31.4%
A 8588
26.2%
V 8550
26.1%
T 1774
 
5.4%
D 1736
 
5.3%
H 1728
 
5.3%
M 30
 
0.1%
N 22
 
0.1%
C 16
 
< 0.1%

sub classification
Categorical

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
Adult
6595 
Pediatric
1955 
HIV test
1567 
HIV test - Ancillary
 
161
Malaria
 
30

Length

Max length20
Median length5
Mean length6.4494382
Min length3

Characters and Unicode

Total characters66584
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHIV test
2nd rowPediatric
3rd rowHIV test
4th rowAdult
5th rowAdult

Common Values

ValueCountFrequency (%)
Adult 6595
63.9%
Pediatric 1955
 
18.9%
HIV test 1567
 
15.2%
HIV test - Ancillary 161
 
1.6%
Malaria 30
 
0.3%
ACT 16
 
0.2%

Length

2025-01-14T21:00:32.352155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-14T21:00:32.407347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
adult 6595
53.3%
pediatric 1955
 
15.8%
hiv 1728
 
14.0%
test 1728
 
14.0%
161
 
1.3%
ancillary 161
 
1.3%
malaria 30
 
0.2%
act 16
 
0.1%

Most occurring characters

ValueCountFrequency (%)
t 12006
18.0%
d 8550
12.8%
l 6947
10.4%
A 6772
10.2%
u 6595
9.9%
i 4101
 
6.2%
e 3683
 
5.5%
a 2206
 
3.3%
r 2146
 
3.2%
c 2116
 
3.2%
Other values (12) 11462
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 12006
18.0%
d 8550
12.8%
l 6947
10.4%
A 6772
10.2%
u 6595
9.9%
i 4101
 
6.2%
e 3683
 
5.5%
a 2206
 
3.3%
r 2146
 
3.2%
c 2116
 
3.2%
Other values (12) 11462
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 12006
18.0%
d 8550
12.8%
l 6947
10.4%
A 6772
10.2%
u 6595
9.9%
i 4101
 
6.2%
e 3683
 
5.5%
a 2206
 
3.3%
r 2146
 
3.2%
c 2116
 
3.2%
Other values (12) 11462
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 12006
18.0%
d 8550
12.8%
l 6947
10.4%
A 6772
10.2%
u 6595
9.9%
i 4101
 
6.2%
e 3683
 
5.5%
a 2206
 
3.3%
r 2146
 
3.2%
c 2116
 
3.2%
Other values (12) 11462
17.2%

vendor
Text

Distinct73
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2025-01-14T21:00:32.550702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length65
Median length13
Mean length18.53332
Min length7

Characters and Unicode

Total characters191338
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.2%

Sample

1st rowRANBAXY Fine Chemicals LTD.
2nd rowAurobindo Pharma Limited
3rd rowAbbott GmbH & Co. KG
4th rowSUN PHARMACEUTICAL INDUSTRIES LTD (RANBAXY LABORATORIES LIMITED)
5th rowAurobindo Pharma Limited
ValueCountFrequency (%)
scms 5404
16.5%
from 5404
16.5%
rdc 5404
16.5%
limited 1288
 
3.9%
ltd 1169
 
3.6%
orgenics 754
 
2.3%
s 717
 
2.2%
buys 715
 
2.2%
wholesaler 715
 
2.2%
laboratories 705
 
2.2%
Other values (158) 10470
32.0%
2025-01-14T21:00:32.773551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22491
 
11.8%
S 16965
 
8.9%
C 13420
 
7.0%
R 11481
 
6.0%
M 8653
 
4.5%
r 8112
 
4.2%
D 7485
 
3.9%
o 7434
 
3.9%
L 7380
 
3.9%
m 6798
 
3.6%
Other values (44) 81119
42.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 191338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22491
 
11.8%
S 16965
 
8.9%
C 13420
 
7.0%
R 11481
 
6.0%
M 8653
 
4.5%
r 8112
 
4.2%
D 7485
 
3.9%
o 7434
 
3.9%
L 7380
 
3.9%
m 6798
 
3.6%
Other values (44) 81119
42.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 191338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22491
 
11.8%
S 16965
 
8.9%
C 13420
 
7.0%
R 11481
 
6.0%
M 8653
 
4.5%
r 8112
 
4.2%
D 7485
 
3.9%
o 7434
 
3.9%
L 7380
 
3.9%
m 6798
 
3.6%
Other values (44) 81119
42.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 191338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22491
 
11.8%
S 16965
 
8.9%
C 13420
 
7.0%
R 11481
 
6.0%
M 8653
 
4.5%
r 8112
 
4.2%
D 7485
 
3.9%
o 7434
 
3.9%
L 7380
 
3.9%
m 6798
 
3.6%
Other values (44) 81119
42.4%
Distinct86
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2025-01-14T21:00:32.896411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length98
Median length60
Mean length22.15091
Min length7

Characters and Unicode

Total characters228686
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.1%

Sample

1st rowHIV, Reveal G3 Rapid HIV-1 Antibody Test
2nd rowNevirapine
3rd rowHIV 1/2, Determine Complete HIV Kit
4th rowLamivudine
5th rowStavudine
ValueCountFrequency (%)
hiv 3055
 
13.6%
kit 1579
 
7.0%
1/2 1524
 
6.8%
disoproxil 1300
 
5.8%
fumarate 1300
 
5.8%
efavirenz 1125
 
5.0%
nevirapine 877
 
3.9%
determine 799
 
3.6%
lamivudine/nevirapine/zidovudine 707
 
3.2%
lamivudine/zidovudine 689
 
3.1%
Other values (148) 9451
42.2%
2025-01-14T21:00:33.085570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 30297
 
13.2%
e 19499
 
8.5%
a 15535
 
6.8%
n 15469
 
6.8%
v 13084
 
5.7%
12082
 
5.3%
r 11537
 
5.0%
o 10997
 
4.8%
d 9170
 
4.0%
t 8291
 
3.6%
Other values (53) 82725
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 228686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 30297
 
13.2%
e 19499
 
8.5%
a 15535
 
6.8%
n 15469
 
6.8%
v 13084
 
5.7%
12082
 
5.3%
r 11537
 
5.0%
o 10997
 
4.8%
d 9170
 
4.0%
t 8291
 
3.6%
Other values (53) 82725
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 228686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 30297
 
13.2%
e 19499
 
8.5%
a 15535
 
6.8%
n 15469
 
6.8%
v 13084
 
5.7%
12082
 
5.3%
r 11537
 
5.0%
o 10997
 
4.8%
d 9170
 
4.0%
t 8291
 
3.6%
Other values (53) 82725
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 228686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 30297
 
13.2%
e 19499
 
8.5%
a 15535
 
6.8%
n 15469
 
6.8%
v 13084
 
5.7%
12082
 
5.3%
r 11537
 
5.0%
o 10997
 
4.8%
d 9170
 
4.0%
t 8291
 
3.6%
Other values (53) 82725
36.2%

brand
Categorical

High correlation  Imbalance 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
Generic
7285 
Determine
799 
Uni-Gold
 
373
Aluvia
 
250
Kaletra
 
165
Other values (43)
1452 

Length

Max length15
Median length7
Mean length7.2879698
Min length3

Characters and Unicode

Total characters75241
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowReveal
2nd rowGeneric
3rd rowDetermine
4th rowGeneric
5th rowGeneric

Common Values

ValueCountFrequency (%)
Generic 7285
70.6%
Determine 799
 
7.7%
Uni-Gold 373
 
3.6%
Aluvia 250
 
2.4%
Kaletra 165
 
1.6%
Norvir 136
 
1.3%
Stat-Pak 115
 
1.1%
Bioline 113
 
1.1%
Truvada 94
 
0.9%
Videx 84
 
0.8%
Other values (38) 910
 
8.8%

Length

2025-01-14T21:00:33.153453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
generic 7285
69.7%
determine 799
 
7.6%
uni-gold 373
 
3.6%
aluvia 250
 
2.4%
kaletra 165
 
1.6%
norvir 136
 
1.3%
videx 125
 
1.2%
stat-pak 115
 
1.1%
bioline 113
 
1.1%
truvada 94
 
0.9%
Other values (40) 995
 
9.5%

Most occurring characters

ValueCountFrequency (%)
e 18044
24.0%
i 10118
13.4%
r 9288
12.3%
n 8958
11.9%
G 7758
10.3%
c 7463
9.9%
t 1628
 
2.2%
a 1599
 
2.1%
l 1331
 
1.8%
o 991
 
1.3%
Other values (38) 8063
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 18044
24.0%
i 10118
13.4%
r 9288
12.3%
n 8958
11.9%
G 7758
10.3%
c 7463
9.9%
t 1628
 
2.2%
a 1599
 
2.1%
l 1331
 
1.8%
o 991
 
1.3%
Other values (38) 8063
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 18044
24.0%
i 10118
13.4%
r 9288
12.3%
n 8958
11.9%
G 7758
10.3%
c 7463
9.9%
t 1628
 
2.2%
a 1599
 
2.1%
l 1331
 
1.8%
o 991
 
1.3%
Other values (38) 8063
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 18044
24.0%
i 10118
13.4%
r 9288
12.3%
n 8958
11.9%
G 7758
10.3%
c 7463
9.9%
t 1628
 
2.2%
a 1599
 
2.1%
l 1331
 
1.8%
o 991
 
1.3%
Other values (38) 8063
10.7%

dosage
Real number (ℝ)

High correlation  Zeros 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.49671
Minimum0
Maximum600
Zeros1736
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2025-01-14T21:00:33.207937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median150
Q3300
95-th percentile600
Maximum600
Range600
Interquartile range (IQR)280

Descriptive statistics

Standard deviation185.29451
Coefficient of variation (CV)0.967612
Kurtosis0.25207311
Mean191.49671
Median Absolute Deviation (MAD)140
Skewness1.0582551
Sum1977012
Variance34334.056
MonotonicityNot monotonic
2025-01-14T21:00:33.261827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
150 2015
19.5%
0 1736
16.8%
200 1487
14.4%
300 1467
14.2%
600 1262
12.2%
10 552
 
5.3%
30 426
 
4.1%
100 301
 
2.9%
20 207
 
2.0%
50 179
 
1.7%
Other values (12) 692
 
6.7%
ValueCountFrequency (%)
0 1736
16.8%
1 54
 
0.5%
2 11
 
0.1%
10 552
 
5.3%
15 38
 
0.4%
20 207
 
2.0%
25 39
 
0.4%
30 426
 
4.1%
40 6
 
0.1%
50 179
 
1.7%
ValueCountFrequency (%)
600 1262
12.2%
500 23
 
0.2%
400 156
 
1.5%
300 1467
14.2%
250 88
 
0.9%
200 1487
14.4%
150 2015
19.5%
133 7
 
0.1%
125 4
 
< 0.1%
100 301
 
2.9%

dosage form
Categorical

High correlation 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
Tablet
3532 
Tablet - FDC
2749 
Test kit
1575 
Capsule
729 
Oral solution
727 
Other values (12)
1012 

Length

Max length34
Median length33
Mean length10.253584
Min length6

Characters and Unicode

Total characters105858
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTest kit
2nd rowOral suspension
3rd rowTest kit
4th rowTablet
5th rowCapsule

Common Values

ValueCountFrequency (%)
Tablet 3532
34.2%
Tablet - FDC 2749
26.6%
Test kit 1575
15.3%
Capsule 729
 
7.1%
Oral solution 727
 
7.0%
Chewable/dispersible tablet - FDC 239
 
2.3%
Oral suspension 214
 
2.1%
Test kit - Ancillary 161
 
1.6%
Chewable/dispersible tablet 146
 
1.4%
Delayed-release capsules 131
 
1.3%
Other values (7) 121
 
1.2%

Length

2025-01-14T21:00:33.329383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tablet 6711
33.3%
3270
16.2%
fdc 3023
15.0%
test 1736
 
8.6%
kit 1736
 
8.6%
oral 970
 
4.8%
solution 755
 
3.7%
capsule 729
 
3.6%
chewable/dispersible 385
 
1.9%
suspension 214
 
1.1%
Other values (8) 654
 
3.2%

Most occurring characters

ValueCountFrequency (%)
e 12083
11.4%
t 11415
10.8%
l 10859
10.3%
9859
9.3%
a 9472
8.9%
T 8062
 
7.6%
b 7567
 
7.1%
s 5234
 
4.9%
C 4137
 
3.9%
i 3728
 
3.5%
Other values (22) 23442
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12083
11.4%
t 11415
10.8%
l 10859
10.3%
9859
9.3%
a 9472
8.9%
T 8062
 
7.6%
b 7567
 
7.1%
s 5234
 
4.9%
C 4137
 
3.9%
i 3728
 
3.5%
Other values (22) 23442
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12083
11.4%
t 11415
10.8%
l 10859
10.3%
9859
9.3%
a 9472
8.9%
T 8062
 
7.6%
b 7567
 
7.1%
s 5234
 
4.9%
C 4137
 
3.9%
i 3728
 
3.5%
Other values (22) 23442
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12083
11.4%
t 11415
10.8%
l 10859
10.3%
9859
9.3%
a 9472
8.9%
T 8062
 
7.6%
b 7567
 
7.1%
s 5234
 
4.9%
C 4137
 
3.9%
i 3728
 
3.5%
Other values (22) 23442
22.1%
Distinct88
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2025-01-14T21:00:33.480135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length72
Median length37
Mean length25.039132
Min length5

Characters and Unicode

Total characters258504
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.2%

Sample

1st rowRanbaxy Fine Chemicals LTD
2nd rowAurobindo Unit III, India
3rd rowABBVIE GmbH & Co.KG Wiesbaden
4th rowRanbaxy, Paonta Shahib, India
5th rowAurobindo Unit III, India
ValueCountFrequency (%)
india 4678
 
11.9%
unit 4197
 
10.7%
iii 4041
 
10.3%
aurobindo 3283
 
8.4%
mylan 1438
 
3.7%
formerly 1415
 
3.6%
matrix 1415
 
3.6%
nashik 1415
 
3.6%
in 1047
 
2.7%
hetero 913
 
2.3%
Other values (201) 15396
39.2%
2025-01-14T21:00:33.709162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28937
 
11.2%
i 20789
 
8.0%
I 19607
 
7.6%
a 18921
 
7.3%
n 18236
 
7.1%
r 13636
 
5.3%
o 12958
 
5.0%
d 12314
 
4.8%
e 10570
 
4.1%
t 10195
 
3.9%
Other values (59) 92341
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 258504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
28937
 
11.2%
i 20789
 
8.0%
I 19607
 
7.6%
a 18921
 
7.3%
n 18236
 
7.1%
r 13636
 
5.3%
o 12958
 
5.0%
d 12314
 
4.8%
e 10570
 
4.1%
t 10195
 
3.9%
Other values (59) 92341
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 258504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
28937
 
11.2%
i 20789
 
8.0%
I 19607
 
7.6%
a 18921
 
7.3%
n 18236
 
7.1%
r 13636
 
5.3%
o 12958
 
5.0%
d 12314
 
4.8%
e 10570
 
4.1%
t 10195
 
3.9%
Other values (59) 92341
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 258504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
28937
 
11.2%
i 20789
 
8.0%
I 19607
 
7.6%
a 18921
 
7.3%
n 18236
 
7.1%
r 13636
 
5.3%
o 12958
 
5.0%
d 12314
 
4.8%
e 10570
 
4.1%
t 10195
 
3.9%
Other values (59) 92341
35.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
True
7030 
False
3294 
ValueCountFrequency (%)
True 7030
68.1%
False 3294
31.9%
2025-01-14T21:00:33.776640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

freight cost (usd)
Real number (ℝ)

High correlation 

Distinct9529
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11015.454
Minimum0.75
Maximum289653.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2025-01-14T21:00:33.833084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.75
5-th percentile787.7315
Q12687.8435
median6397.5225
Q314032.869
95-th percentile34721.667
Maximum289653.2
Range289652.45
Interquartile range (IQR)11345.025

Descriptive statistics

Standard deviation14652.048
Coefficient of variation (CV)1.3301357
Kurtosis43.027716
Mean11015.454
Median Absolute Deviation (MAD)4569.887
Skewness4.7521843
Sum1.1372354 × 108
Variance2.1468252 × 108
MonotonicityNot monotonic
2025-01-14T21:00:33.903204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9736.1 40
 
0.4%
6147.18 31
 
0.3%
13398.06 17
 
0.2%
7445.8 16
 
0.2%
9341.49 15
 
0.1%
25231.96 12
 
0.1%
7329.83 12
 
0.1%
1211.48 11
 
0.1%
11637.64 11
 
0.1%
15128.37 11
 
0.1%
Other values (9519) 10148
98.3%
ValueCountFrequency (%)
0.75 1
< 0.1%
14.36 1
< 0.1%
17.72 1
< 0.1%
22.29 1
< 0.1%
29.21 1
< 0.1%
30 1
< 0.1%
30.49 1
< 0.1%
41 1
< 0.1%
42.35 1
< 0.1%
48 1
< 0.1%
ValueCountFrequency (%)
289653.2 1
< 0.1%
241407.27 1
< 0.1%
220076.52 1
< 0.1%
194623.44 1
< 0.1%
184243.3725 1
< 0.1%
161962.32 1
< 0.1%
161712.87 1
< 0.1%
152368.7 1
< 0.1%
150499.84 1
< 0.1%
146850.66 1
< 0.1%

reliability
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean366.08485
Minimum1
Maximum768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2025-01-14T21:00:33.973513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q1145
median340
Q3596
95-th percentile768
Maximum768
Range767
Interquartile range (IQR)451

Descriptive statistics

Standard deviation238.18725
Coefficient of variation (CV)0.65063401
Kurtosis-1.1198953
Mean366.08485
Median Absolute Deviation (MAD)201
Skewness0.18250127
Sum3779460
Variance56733.168
MonotonicityNot monotonic
2025-01-14T21:00:34.045053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
768 768
 
7.4%
729 729
 
7.1%
628 628
 
6.1%
596 596
 
5.8%
522 522
 
5.1%
450 450
 
4.4%
431 431
 
4.2%
406 406
 
3.9%
369 369
 
3.6%
340 340
 
3.3%
Other values (57) 5085
49.3%
ValueCountFrequency (%)
1 12
 
0.1%
2 20
 
0.2%
3 39
0.4%
4 24
 
0.2%
5 30
 
0.3%
6 18
 
0.2%
7 21
 
0.2%
8 48
0.5%
9 18
 
0.2%
10 80
0.8%
ValueCountFrequency (%)
768 768
7.4%
729 729
7.1%
628 628
6.1%
596 596
5.8%
522 522
5.1%
450 450
4.4%
431 431
4.2%
406 406
3.9%
369 369
3.6%
340 340
3.3%

Delivery Delay Risk
Real number (ℝ)

Zeros 

Distinct76
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.15689395
Minimum-1
Maximum1
Zeros6324
Zeros (%)61.3%
Negative2814
Negative (%)27.3%
Memory size80.8 KiB
2025-01-14T21:00:34.116516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-0.90514825
median0
Q30
95-th percentile0.99999834
Maximum1
Range2
Interquartile range (IQR)0.90514825

Descriptive statistics

Standard deviation0.5833083
Coefficient of variation (CV)-3.7178509
Kurtosis-0.32082862
Mean-0.15689395
Median Absolute Deviation (MAD)0
Skewness0.044119761
Sum-1619.7731
Variance0.34024858
MonotonicityNot monotonic
2025-01-14T21:00:34.189904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6324
61.3%
-1 698
 
6.8%
1 188
 
1.8%
-0.9999999172 127
 
1.2%
-0.9950547537 119
 
1.2%
-0.9993292997 112
 
1.1%
-0.9999954794 107
 
1.0%
-0.9981778976 106
 
1.0%
-0.761594156 93
 
0.9%
0.4621171573 92
 
0.9%
Other values (66) 2358
 
22.8%
ValueCountFrequency (%)
-1 698
6.8%
-1 11
 
0.1%
-1 25
 
0.2%
-1 22
 
0.2%
-1 32
 
0.3%
-1 12
 
0.1%
-1 26
 
0.3%
-1 77
 
0.7%
-1 14
 
0.1%
-1 28
 
0.3%
ValueCountFrequency (%)
1 188
1.8%
1 4
 
< 0.1%
1 9
 
0.1%
1 10
 
0.1%
1 3
 
< 0.1%
1 6
 
0.1%
1 10
 
0.1%
1 13
 
0.1%
1 8
 
0.1%
1 7
 
0.1%

weight
Real number (ℝ)

High correlation  Skewed 

Distinct6450
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3376.4822
Minimum0
Maximum857354
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2025-01-14T21:00:34.445836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1314.75
median1221
Q33381.25
95-th percentile12358.05
Maximum857354
Range857354
Interquartile range (IQR)3066.5

Descriptive statistics

Standard deviation11365.3
Coefficient of variation (CV)3.3660181
Kurtosis3148.0062
Mean3376.4822
Median Absolute Deviation (MAD)1085.4615
Skewness44.785754
Sum34858802
Variance1.2917005 × 108
MonotonicityNot monotonic
2025-01-14T21:00:34.510268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 29
 
0.3%
6 27
 
0.3%
1 23
 
0.2%
60 21
 
0.2%
12 20
 
0.2%
5 20
 
0.2%
4 19
 
0.2%
21 19
 
0.2%
17 19
 
0.2%
14 19
 
0.2%
Other values (6440) 10108
97.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 23
0.2%
2 29
0.3%
3 18
0.2%
4 19
0.2%
4.5 1
 
< 0.1%
5 20
0.2%
6 27
0.3%
7 16
0.2%
7.5 2
 
< 0.1%
ValueCountFrequency (%)
857354 1
< 0.1%
291096 1
< 0.1%
205503 1
< 0.1%
154780 1
< 0.1%
145719.5 1
< 0.1%
112027 1
< 0.1%
102929 1
< 0.1%
90446 1
< 0.1%
88761 1
< 0.1%
88190 1
< 0.1%

profit
Real number (ℝ)

Distinct9371
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0052752 × 108
Minimum0
Maximum4.1696609 × 1010
Zeros54
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2025-01-14T21:00:34.578969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51.52
Q131884.054
median1436794.2
Q339453344
95-th percentile7.2467663 × 108
Maximum4.1696609 × 1010
Range4.1696609 × 1010
Interquartile range (IQR)39421459

Descriptive statistics

Standard deviation1.210646 × 109
Coefficient of variation (CV)6.0373058
Kurtosis365.12567
Mean2.0052752 × 108
Median Absolute Deviation (MAD)1436744.4
Skewness16.21943
Sum2.0702462 × 1012
Variance1.4656637 × 1018
MonotonicityNot monotonic
2025-01-14T21:00:34.654510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54
 
0.5%
83617116.24 15
 
0.1%
66000000 13
 
0.1%
16384 12
 
0.1%
501760 10
 
0.1%
64000000 10
 
0.1%
853820.352 9
 
0.1%
576960400 8
 
0.1%
121506969.9 8
 
0.1%
122.5 7
 
0.1%
Other values (9361) 10178
98.6%
ValueCountFrequency (%)
0 54
0.5%
0.0276 1
 
< 0.1%
0.0374 1
 
< 0.1%
0.0405 1
 
< 0.1%
0.0442 1
 
< 0.1%
0.045 1
 
< 0.1%
0.0477 1
 
< 0.1%
0.0497 1
 
< 0.1%
0.0506 1
 
< 0.1%
0.055 1
 
< 0.1%
ValueCountFrequency (%)
4.169660923 × 10101
< 0.1%
3.420964588 × 10101
< 0.1%
3.031636951 × 10101
< 0.1%
2.920316677 × 10101
< 0.1%
2.716057189 × 10101
< 0.1%
2.528271154 × 10101
< 0.1%
2.211915863 × 10101
< 0.1%
1.993079448 × 10101
< 0.1%
1.882138799 × 10101
< 0.1%
1.804335738 × 10101
< 0.1%

Interactions

2025-01-14T21:00:30.608660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:28.793557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.103976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.427855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.937937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.290940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.661401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:28.844379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.154772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.481158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.996938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.340985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.717978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:28.897425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.207852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.713097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.055668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.394691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.776223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:28.951136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.266523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.765345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.120787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.449882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.833719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.007033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.325988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.825285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.180918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.508117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.881908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.054564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.376943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:29.881388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.234890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-01-14T21:00:30.558524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-01-14T21:00:34.709466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Delivery Delay Riskbrandcountrydosagedosage formfirst line designationfreight cost (usd)fulfill viamanaged byproduct groupprofitreliabilityshipment modesub classificationvendor inco termweight
Delivery Delay Risk1.0000.1330.150-0.0980.1350.076-0.0190.4970.0240.1250.008-0.1190.2180.1130.209-0.016
brand0.1331.0000.1990.5430.5150.2450.0470.5070.1630.7810.0000.1890.2100.6470.4430.000
country0.1500.1991.0000.1750.1940.3210.0510.6100.0980.3130.0000.7260.5350.3100.4410.000
dosage-0.0980.5430.1751.0000.4610.1340.0440.3770.0000.4350.2110.2420.1750.4670.2780.186
dosage form0.1350.5150.1940.4611.0000.2300.0250.4760.0190.6530.0320.1800.2350.8250.2880.000
first line designation0.0760.2450.3210.1340.2301.0000.0290.0990.0990.1920.0270.2220.2330.2070.3090.000
freight cost (usd)-0.0190.0470.0510.0440.0250.0291.0000.0240.0000.0000.3590.1170.0380.0330.0190.706
fulfill via0.4970.5070.6100.3770.4760.0990.0241.0000.0780.3850.0640.4020.3610.3941.0000.000
managed by0.0240.1630.0980.0000.0190.0990.0000.0781.0000.0180.0000.1030.0690.0180.1050.000
product group0.1250.7810.3130.4350.6530.1920.0000.3850.0181.0000.0000.1940.1850.8660.3220.000
profit0.0080.0000.0000.2110.0320.0270.3590.0640.0000.0001.000-0.0250.0690.0090.0000.469
reliability-0.1190.1890.7260.2420.1800.2220.1170.4020.1030.194-0.0251.0000.5480.1970.2690.119
shipment mode0.2180.2100.5350.1750.2350.2330.0380.3610.0690.1850.0690.5481.0000.1940.3660.000
sub classification0.1130.6470.3100.4670.8250.2070.0330.3940.0180.8660.0090.1970.1941.0000.2840.000
vendor inco term0.2090.4430.4410.2780.2880.3090.0191.0000.1050.3220.0000.2690.3660.2841.0000.025
weight-0.0160.0000.0000.1860.0000.0000.7060.0000.0000.0000.4690.1190.0000.0000.0251.000

Missing values

2025-01-14T21:00:30.961118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-14T21:00:31.104666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

project codecountrymanaged byfulfill viavendor inco termshipment modeproduct groupsub classificationvendormolecule/test typebranddosagedosage formmanufacturing sitefirst line designationfreight cost (usd)reliabilityDelivery Delay Riskweightprofit
0100-CI-T01Côte d'IvoirePMO - USDirect DropEXWAirHRDTHIV testRANBAXY Fine Chemicals LTD.HIV, Reveal G3 Rapid HIV-1 Antibody TestReveal0.0Test kitRanbaxy Fine Chemicals LTDTrue780.340400.013.025919.040
1108-VN-T01VietnamPMO - USDirect DropEXWAirARVPediatricAurobindo Pharma LimitedNevirapineGeneric10.0Oral suspensionAurobindo Unit III, IndiaTrue4521.5001660.0358.0291648.000
2100-CI-T01Côte d'IvoirePMO - USDirect DropFCAAirHRDTHIV testAbbott GmbH & Co. KGHIV 1/2, Determine Complete HIV KitDetermine0.0Test kitABBVIE GmbH & Co.KG WiesbadenTrue1653.780400.0171.01881600.000
3108-VN-T01VietnamPMO - USDirect DropEXWAirARVAdultSUN PHARMACEUTICAL INDUSTRIES LTD (RANBAXY LABORATORIES LIMITED)LamivudineGeneric150.0TabletRanbaxy, Paonta Shahib, IndiaTrue16007.0601660.01855.05991052.032
4108-VN-T01VietnamPMO - USDirect DropEXWAirARVAdultAurobindo Pharma LimitedStavudineGeneric30.0CapsuleAurobindo Unit III, IndiaTrue45450.0801660.07590.05720064.000
5112-NG-T01NigeriaPMO - USDirect DropEXWAirARVPediatricAurobindo Pharma LimitedZidovudineGeneric10.0Oral solutionAurobindo Unit III, IndiaTrue5920.4201390.0504.0104692.224
6110-ZM-T01ZambiaPMO - USDirect DropDDUAirARVPediatricMERCK SHARP & DOHME IDEA GMBH (FORMALLY MERCK SHARP & DOHME B.V.)EfavirenzStocrin/Sustiva200.0CapsuleMSD South Granville AustraliaTrue6066.4152600.0328.0205752.960
7109-TZ-T01TanzaniaPMO - USDirect DropEXWAirARVAdultAurobindo Pharma LimitedNevirapineGeneric200.0TabletAurobindo Unit III, IndiaTrue6212.4101350.01478.02861657.232
8112-NG-T01NigeriaPMO - USDirect DropEXWAirARVAdultAurobindo Pharma LimitedStavudineGeneric30.0CapsuleAurobindo Unit III, IndiaFalse8051.6801390.01060.525041.744
9110-ZM-T01ZambiaPMO - USDirect DropCIPAirARVAdultABBVIE LOGISTICS (FORMERLY ABBOTT LOGISTICS BV)Lopinavir/RitonavirAluvia200.0TabletABBVIE (Abbott) St. P'burg USATrue9890.9502600.0643.05413363.200
project codecountrymanaged byfulfill viavendor inco termshipment modeproduct groupsub classificationvendormolecule/test typebranddosagedosage formmanufacturing sitefirst line designationfreight cost (usd)reliabilityDelivery Delay Riskweightprofit
10314151-NG-T30NigeriaPMO - USFrom RDCN/A - From RDCAir CharterARVPediatricSCMS from RDCLamivudine/Nevirapine/ZidovudineGeneric30.0Chewable/dispersible tablet - FDCMylan (formerly Matrix) NashikFalse21701.144628-1.00000012609.0000001.424562e+06
10315151-NG-T30NigeriaPMO - USFrom RDCN/A - From RDCAir CharterARVAdultSCMS from RDCLopinavir/RitonavirAluvia200.0TabletABBVIE Ludwigshafen GermanyTrue26180.000628-1.00000015198.0000001.750167e+09
10316151-NG-T30NigeriaPMO - USFrom RDCN/A - From RDCAir CharterARVAdultSCMS from RDCLamivudine/ZidovudineGeneric150.0Tablet - FDCAurobindo Unit III, IndiaTrue3410.000628-1.0000001547.0000001.125776e+07
10317151-NG-T30NigeriaPMO - USFrom RDCN/A - From RDCAirARVAdultSCMS from RDCEfavirenzGeneric600.0TabletStrides, Bangalore, India.False3410.000628-0.9993291521.1666675.179685e+05
10318103-ZW-T30ZimbabwePMO - USFrom RDCN/A - From RDCTruckARVPediatricSCMS from RDCLamivudine/Nevirapine/ZidovudineGeneric30.0Chewable/dispersible tablet - FDCCipla, Goa, IndiaFalse3410.000101-1.0000001495.3333336.425699e+08
10319103-ZW-T30ZimbabwePMO - USFrom RDCN/A - From RDCTruckARVPediatricSCMS from RDCLamivudine/Nevirapine/ZidovudineGeneric30.0Chewable/dispersible tablet - FDCMylan, H-12 & H-13, IndiaFalse3410.000101-1.0000001469.5000004.232309e+08
10320104-CI-T30Côte d'IvoirePMO - USFrom RDCN/A - From RDCTruckARVAdultSCMS from RDCLamivudine/ZidovudineGeneric150.0Tablet - FDCHetero Unit III Hyderabad INFalse3410.0007290.9950551443.6666672.221725e+07
10321110-ZM-T30ZambiaPMO - USFrom RDCN/A - From RDCTruckARVAdultSCMS from RDCEfavirenz/Lamivudine/Tenofovir Disoproxil FumarateGeneric600.0Tablet - FDCCipla Ltd A-42 MIDC Mahar. INFalse3410.000406-0.9950551417.8333332.716057e+10
10322200-ZW-T30ZimbabwePMO - USFrom RDCN/A - From RDCTruckARVAdultSCMS from RDCLamivudine/ZidovudineGeneric150.0Tablet - FDCMylan (formerly Matrix) NashikTrue3410.00028-1.0000001392.0000001.526224e+07
10323103-ZW-T30ZimbabwePMO - USFrom RDCN/A - From RDCTruckARVPediatricSCMS from RDCLamivudine/ZidovudineGeneric30.0Chewable/dispersible tablet - FDCCipla, Goa, IndiaFalse3410.000101-1.0000001392.0000006.257274e+06

Duplicate rows

Most frequently occurring

project codecountrymanaged byfulfill viavendor inco termshipment modeproduct groupsub classificationvendormolecule/test typebranddosagedosage formmanufacturing sitefirst line designationfreight cost (usd)reliabilityDelivery Delay Riskweightprofit# duplicates
4107-RW-T30RwandaPMO - USDirect DropEXWAirHRDTHIV testSHANGHAI KEHUA BIOENGINEERING CO.,LTD. (KHB)HIV 1/2, Colloidal Gold, Diagnostic Kit, AntibodyColloidal Gold0.0Test kitKHB Test Kit Facility, Shanghai ChinaTrue26266.343400.0000001992.06.629535e+065
0100-CI-T01Côte d'IvoirePMO - USFrom RDCN/A - From RDCAirARVAdultSCMS from RDCLamivudine/Nevirapine/StavudineGeneric150.0Tablet - FDCCipla, Goa, IndiaTrue3166.00400.000000974.03.175200e+063
16133-NG-T30NigeriaPMO - USDirect DropEXWAirHRDTHIV testOrgenics, LtdHIV 1/2, Determine Complete HIV KitDetermine0.0Test kitAlere Medical Co., Ltd.True24927.19145-0.4621171186.08.361712e+073
19133-NG-T30NigeriaPMO - USDirect DropEXWAirHRDTHIV testOrgenics, LtdHIV 1/2, Determine Complete HIV KitDetermine0.0Test kitAlere Medical Co., Ltd.True28734.43145-0.4621171303.08.361712e+073
1101-CD-T30Congo, DRCPMO - USFrom RDCN/A - From RDCAirARVAdultSCMS from RDCLamivudineGeneric150.0TabletHetero Unit III Hyderabad INTrue522.423080.0000001.04.287200e+002
2102-BW-T01BotswanaPMO - USDirect DropEXWAirHRDTHIV testTrinity Biotech, PlcHIV 1/2, Uni-Gold HIV KitUni-Gold0.0Test kitTrinity Biotech, PlcTrue2442.32300.000000500.04.096000e+052
3104-CI-T01Côte d'IvoirePMO - USFrom RDCN/A - From RDCAirARVPediatricSCMS from RDCNevirapineGeneric10.0Oral suspensionAurobindo Unit III, IndiaTrue1301.003140.000000170.01.331520e+032
5107-RW-T30RwandaPMO - USDirect DropEXWAirHRDTHIV testSHANGHAI KEHUA BIOENGINEERING CO.,LTD. (KHB)HIV 1/2, Colloidal Gold, Diagnostic Kit, AntibodyColloidal Gold0.0Test kitKHB Test Kit Facility, Shanghai ChinaTrue53062.213400.0000003996.02.216893e+072
6108-VN-T01VietnamPMO - USDirect DropFCAAirARVAdultABBVIE LOGISTICS (FORMERLY ABBOTT LOGISTICS BV)Lopinavir/RitonavirAluvia200.0TabletABBVIE Ludwigshafen GermanyTrue1767.381660.000000117.02.701996e+062
7108-VN-T30VietnamPMO - USDirect DropEXWAirARVAdultHETERO LABS LIMITEDEfavirenz/Lamivudine/Tenofovir Disoproxil FumarateGeneric600.0Tablet - FDCHetero Unit III Hyderabad INTrue3809.945220.0000002516.03.712550e+072